Conversational analytics in the Interaction Portal
The following video shows a customer interaction and the resulting feedback from conversational analytics. Conversational analytics in Pega Customer Service™ applies AI-powered scoring and feedback mechanisms to evaluate the quality and effectiveness of customer interactions. The system analyzes voice and digital conversations to generate metrics such as interaction effort, resolution prediction, and sentiment score. These metrics help customer service representatives (CSRs) identify areas for improvement and ensure compliance with service standards.
Script
This video shows the wrap-up of a voice interaction between Jason, a customer service representative (CSR), and Sarah Connor, a customer.
When Jason clicks Wrap-up, the Customer composite view shows the Interaction Summary that lists the main issues covered in the conversation. Sarah called about moving her communication services to a new address. Jason processed the address change and provided information about possible fees and responded to Sarah's question about a phone trade-in. He created a Complaint Case in response to an experience Sarah had at UComms retail store, and he apologized for the negative interaction with a retail associate.
Jason clicks the Feedback tab to review his scores for the preconfigured metrics.
Interaction effort measures the ease of resolution for the customer issue.
Resolution Prediction measures the likelihood that the customer issues are resolved in the first interaction.
Customer sentiment score tracks emotions expressed by the customer or the CSR.
These scores provide an immediate response so that Jason can assess the interaction and note areas for improvement.
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